훈련용 데이터 정답용 데이터로 모델을 훈련 Epoch 1/30 14/14 [==============================] - 0s 2ms/step - loss: 0.2619 - accuracy: 0.9107 Epoch 2/30 14/14 [==============================] - 0s 2ms/step - loss: 0.2560 - accuracy: 0.9107 Epoch 3/30 14/14 [==============================] - 0s 2ms/step - loss: 0.2505 - accuracy: 0.9107 Epoch 4/30 14/14 [==============================] - 0s 2ms/step - loss: 0.2460 - accuracy: 0.9196 Epoch 5/30 14/14 [==============================] - 0s 2ms/step - loss: 0.2408 - accuracy: 0.9196 Epoch 6/30 14/14 [==============================] - 0s 1ms/step - loss: 0.2361 - accuracy: 0.9196 Epoch 7/30 14/14 [==============================] - 0s 2ms/step - loss: 0.2318 - accuracy: 0.9286 Epoch 8/30 14/14 [==============================] - 0s 2ms/step - loss: 0.2273 - accuracy: 0.9107 Epoch 9/30 14/14 [==============================] - 0s 2ms/step - loss: 0.2226 - accuracy: 0.9286 Epoch 10/30 14/14 [==============================] - 0s 2ms/step - loss: 0.2182 - accuracy: 0.9286 Epoch 11/30 14/14 [==============================] - 0s 2ms/step - loss: 0.2142 - accuracy: 0.9375 Epoch 12/30 14/14 [==============================] - 0s 2ms/step - loss: 0.2104 - accuracy: 0.9286 Epoch 13/30 14/14 [==============================] - 0s 2ms/step - loss: 0.2064 - accuracy: 0.9375 Epoch 14/30 14/14 [==============================] - 0s 2ms/step - loss: 0.2026 - accuracy: 0.9375 Epoch 15/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1993 - accuracy: 0.9375 Epoch 16/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1959 - accuracy: 0.9375 Epoch 17/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1922 - accuracy: 0.9375 Epoch 18/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1884 - accuracy: 0.9375 Epoch 19/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1853 - accuracy: 0.9375 Epoch 20/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1821 - accuracy: 0.9464 Epoch 21/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1789 - accuracy: 0.9464 Epoch 22/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1755 - accuracy: 0.9375 Epoch 23/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1735 - accuracy: 0.9375 Epoch 24/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1702 - accuracy: 0.9554 Epoch 25/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1669 - accuracy: 0.9464 Epoch 26/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1642 - accuracy: 0.9464 Epoch 27/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1619 - accuracy: 0.9464 Epoch 28/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1588 - accuracy: 0.9464 Epoch 29/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1565 - accuracy: 0.9464 Epoch 30/30 14/14 [==============================] - 0s 2ms/step - loss: 0.1544 - accuracy: 0.9464
'책 > 구글코랩으로 배우는 인공지능기술(23.01.26-23.02.04)' 카테고리의 다른 글
| chapter09 변분 오토 인코더VAE (0) | 2023.02.01 |
|---|---|
| chapter08 순환신경망 RNN (0) | 2023.01.29 |
| chapter07 합성곱 신경망 CNN (0) | 2023.01.29 |
| chapter06 다양한 머신러닝 방법 (0) | 2023.01.28 |
| chapter05 딥러닝의 이론 (0) | 2023.01.27 |